Comments (1)
The current code does not support multi-gpu. It needs to be modified for that purpose. However, multi-gpu is not recommended here since currently the batchsize is already around 200. If you want to train faster you can reduce the epochs, e.g. change to 150 or 200. The performance should not hurt much.
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Related Issues (20)
- Array size mismatch when running `predict_ros.py` HOT 3
- Performance on textureless objects HOT 3
- docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]]. ERRO[0000] error waiting for container: HOT 2
- problem in getting inference on novel objects using python predict_ros.py inside docker HOT 3
- clarification regarding synthetic data generation HOT 1
- error in running python blender_main.py -- Color management: image colorspace "sRGB OETF" not found, will use default instead. HOT 2
- Failed to initialize Pyglet window with an OpenGL >= 3+ context. HOT 7
- Dockerfile HOT 1
- ValueError: could not broadcast input array from shape (226,501,3) into shape (226,0,3) HOT 2
- Data Download links seem broken? HOT 1
- Problem generating train pair data HOT 1
- Lie Algebra ordering possibly incorrect
- texture_folders HOT 1
- IndexError: list index out of range HOT 6
- predict_ros.py error finding model path HOT 3
- RuntimeError: CUDA error: no kernel image is available for execution on the device HOT 7
- Blender script not working with newer Blender versions HOT 4
- building the dockerfile didn't work HOT 4
- File "/home/posetrack/blender_dataset_generator.py", line 303, in generate assert len(texture_files)>0 AssertionError HOT 1
- RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU. HOT 6
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